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Course Highlights

Comprehensive Learning Path: Covers foundational to intermediate concepts of Machine Learning with AWS, designed as a structured journey for learners.

Industry-relevant Skills: Aligned to National Occupational Standards (NOS), ensuring applicability to real-world ML roles.

Certification Readiness: Prepares learners for AWS Machine Learning specialty certifications by building core competencies.

Key Topics Covered:

  • Introduction to Machine Learning concepts and workflows
  • Data preparation, cleaning, and transformation
  • Model training, evaluation, and deployment on AWS
  • Supervised and unsupervised learning techniques
  • Deep learning basics with AWS services (SageMaker, Rekognition, Comprehend, and more)

Learner Outcomes:

  • Ability to build, train, and deploy ML models on AWS
  • Understanding of ML lifecycle management on the cloud
  • Skills validated through SSC Nasscom's NOS-aligned framework

  • Skill Type

  • Course Duration

  • Domain

  • GOI Incentive applicable

  • Course Category

  • Nasscom Assessment

  • Placement Assistance

  • Certificate Earned

  • Badge Earned

  • Content Alignment Type

  • NOS Details

  • Mode of Delivery

Course Details

Learning Objectives

By the end of this Learning Journey, learners will be able to:

  • Understand the fundamentals of Machine Learning
  • Explain core ML concepts, types of learning (supervised, unsupervised, reinforcement), and the ML development lifecycle
  • Recognise common ML use cases across industries
  • Build foundational skills on AWS for Machine Learning
  • Explore AWS services relevant to data preparation, model building, training, and deployment (e.g., Amazon SageMaker, AWS Lambda, Amazon S3, AWS Glue)
  • Navigate AWS ML tools and apply them to solve basic business problems
  • Work with data for ML
  • Ingest, clean, and prepare datasets using AWS tools
  • Apply feature engineering techniques to improve model accuracy
  • Develop and train Machine Learning models
  • Build, train, and evaluate ML models in Amazon SageMaker
  • Select appropriate ML algorithms based on the problem statement
  • Deploy and operationalise ML models
  • Deploy trained models to production using AWS ML services
  • Monitor and optimise deployed models for scalability and efficiency
  • Apply Responsible AI and ML best practices
  • Understand concepts of fairness, ethics, bias, and explainability in ML
  • Apply security and compliance best practices while handling data on AWS
  • Demonstrate Industry Readiness
  • Solve real-world ML scenarios using AWS tools
  • Prepare for AWS Certification pathways related to Machine Learning
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Reasons to enrol

The AWS Machine Learning Journey is designed to equip learners with the skills and knowledge required to apply Machine Learning (ML) to real-world business challenges. By enrolling in this programme, you will:

  • Build In-demand Skills: Gain hands-on experience with AWS ML services, frameworks, and tools that are widely adopted across industries.
  • Structured Learning Path: Progress through a carefully curated set of courses that start with foundational concepts and advance towards practical applications - ensuring strong conceptual understanding and job-ready skills.
  • Industry-aligned Content: The journey is aligned with National Occupational Standards (NOS) under the Deep Skilling category, ensuring recognition of your learning outcomes.
  • Practical Applications: Learn to build, train, and deploy ML models on AWS and understand how ML can drive digital transformation across sectors.
  • Certification-ready: Completion of this journey prepares you for AWS’s role-based certifications in ML and AI, enhancing your employability.
  • Future-ready Careers: Machine Learning is one of the fastest-growing digital skill areas; mastering it opens opportunities in Data Science, AI Engineering, and ML Operations.

This journey is ideal for students, early professionals, and working practitioners seeking to strengthen their ML expertise and stand out in today’s competitive job market.

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Ideal Participants

This Learning Journey is designed for learners who want to build foundational to intermediate skills in Machine Learning (ML) using AWS services. It is suitable for:

  • Students and early-career professionals from Engineering, Computer Science, Data Science, and related fields who want to explore a career in Artificial Intelligence (AI) and Machine Learning.
  • IT Professionals, Developers, and Data Analysts looking to upskill or reskill in ML concepts, tools, and Cloud-based implementations on AWS.
  • Working Professionals aiming to apply Machine Learning to solve real-world business challenges and strengthen their expertise in Digital Technologies.
  • Educators and Trainers who wish to gain industry-aligned knowledge in ML to enhance their teaching and training content.
  • Career Returnees and Non-technical Learners with a strong interest in ML who want structured exposure to AWS ML services and practical use cases (basic programming knowledge recommended).
  • This journey provides industry-recognised skills that align with National Occupational Standards (NOS) under the Deep Skilling category, helping learners prepare for future job roles in AI/ML, Data Science, and Cloud.
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Curriculum

Curriculum

This journey is aligned to Deep Skilling under NOS standards, designed to build comprehensive skills in Machine Learning using AWS tools, services, and best practices. The curriculum integrates multiple AWS Digital Training modules into a single structured learning pathway.

  • Journey Modules
  • Introduction to Machine Learning on AWS
  • Fundamentals of ML
  • AWS Services for ML Overview (Amazon SageMaker, Rekognition, Comprehend, and more)
  • Real-world Use Cases
  • Getting started with Amazon SageMaker
  • Preparing and Managing Datasets
  • Building, Training, and Deploying ML Models
  • Machine Learning Security and Best Practices
  • Data Privacy and Compliance on AWS
  • Responsible AI Practices
  • Model Monitoring and Governance
  • Deep Dive: Specialised ML Services
  • Computer Vision with Amazon Rekognition
  • Natural Language Processing with Amazon Comprehend
  • Forecasting and Personalisation Services
  • Practical Application and Use Cases
  • Building ML Pipelines on AWS
  • Integrating ML Services into Applications
  • End-to-end ML Model Deployment using AWS Tools
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skills and tools

Learners undertaking this journey will gain hands-on exposure to the foundational concepts, frameworks, and tools used in Building, Training, and Deploying Machine Learning (ML) models on AWS. The journey is designed to provide both theoretical understanding and applied skills, enabling learners to translate business problems into ML-driven solutions.

Skills Acquired

  • Understanding the fundamentals of Artificial Intelligence (AI) and Machine Learning (ML)
  • Data preparation, cleaning, and transformation for ML workflows
  • Feature engineering and model evaluation techniques
  • Supervised and unsupervised learning approaches
  • Model training, optimisation, and deployment on AWS
  • ML workflow automation and best practices
  • Interpreting results and improving model performance
  • Applying ML concepts to real-world industry use cases

Tools and Services Covered

  • Amazon SageMaker - for building, training, and deploying ML models at scale
  • AWS Deep Learning AMIs and Deep Learning Containers - for using popular frameworks like TensorFlow, PyTorch, and MXNet
  • Amazon Rekognition - image and video analysis
  • Amazon Comprehend - Natural Language Processing (NLP)
  • Amazon Polly and Amazon Transcribe - speech services
  • AWS Glue and Amazon S3 - data preparation, storage, and integration
  • Amazon Athena and QuickSight - for data querying and visualisation
  • AWS Lambda - for serverless ML model execution
  • Amazon Machine Learning SDKs and APIs - to integrate ML into applications
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